Sketch-based 3D reconstruction remains a challenging task due to the abstract and sparse nature of sketch inputs, which often lack sufficient semantic and geometric information. To address this, we propose Sketch2Symm, a two-stage generation method that produces geometrically consistent 3D shapes from sketches. Our approach introduces semantic bridging via sketch-to-image translation to enrich sparse sketch representations, and incorporates symmetry constraints as geometric priors to leverage the structural regularity commonly found in everyday objects. Experiments on mainstream sketch datasets demonstrate that our method achieves superior performance compared to existing sketch-based reconstruction methods in terms of Chamfer Distance, Earth Mover's Distance, and F-Score, verifying the effectiveness of the proposed semantic bridging and symmetry-aware design.

翻译：基于草图的3D重建因其输入的抽象性与稀疏性而始终是一项具有挑战性的任务，草图通常缺乏足够的语义与几何信息。为解决此问题，我们提出了Sketch2Symm，一种两阶段生成方法，能够从草图生成几何一致的3D形状。我们的方法通过草图到图像的转换引入语义桥接，以丰富稀疏的草图表示，并融入对称约束作为几何先验，以利用日常物体中普遍存在的结构规律性。在主流草图数据集上的实验表明，与现有的基于草图的重建方法相比，我们的方法在倒角距离、推土机距离和F分数方面均取得了更优的性能，验证了所提出的语义桥接与对称感知设计的有效性。